Construction of a nursing sensitive quality indicator evaluation system for sepsis care quality: A three-stage mixed-method Delphi study

Construction of a nursing sensitive quality indicator evaluation system for sepsis care quality: A three-stage mixed-method Delphi study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of a nursing sensitive quality indicator evaluation system for sepsis care quality: A three-stage mixed-method Delphi study Chen Huang, Huan Liu, Wenwen Qi, Shuyuan Zhao, Jinghua Xu, Feng Jing, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8146381/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Currently, there are no objective, scientific, and sensitive assessment frameworks for evaluating sepsis care quality management in China. This study aimed to address this gap by developing and validating nursing-sensitive quality indicators (NSQIs) tailored to sepsis management in Chinese clinical settings. Methods This mixed-methods investigation involved three phases. A systematic literature review (2014–2024) across four databases identified evidence regarding sepsis care quality. Subsequently, semi-structured interviews were conducted with 16 clinical experts from emergency departments (EDs) and intensive care units (ICUs) to examine their perspectives on NSQIs. Finally, a modified Delphi process engaged a multidisciplinary panel to refine and validate sepsis-specific NSQIs through a systematic consensus-building process. Result Two rounds of expert consultation were completed, with a 100% questionnaire return rate. Sixteen experts (10 nurses and six physicians) participated in the first and second rounds of the Delphi survey. The mean score of the expert authority coefficient for the two rounds was 0.96. The range of Kendall W values was 0.120–0.316 (P < 0.001). A comprehensive list of sensitive indicators for sepsis care quality was established, encompassing three primary, nine secondary, and 30 tertiary indicators. Conclusion The established NSQIs encompass three fundamental dimensions of sepsis care quality: importance, rationality, and operational feasibility. This two-round Delphi process achieved expert consensus, confirming the system’s alignment with clinical guidelines and feasibility. Clinical practice implications: This research provides a valuable framework for evaluating clinical care quality in sepsis management. Delphi method sepsis management nursing-sensitive quality indicator Figures Figure 1 Introduction Sepsis is a life-threatening organ dysfunction caused by an uncontrolled immune response to infection. It represents a significant global health challenge, accounting for 19.7% of annual deaths worldwide [ 1 , 2 ]. Common symptoms of sepsis include drowsiness, chills or fever, hypothermia, nausea, low blood pressure, and a rapid heartbeat [ 3 ]. Patients with sepsis may also experience shock, multiple organ failure, decreased urine output, and acute respiratory distress, all of which can lead to death if the condition worsens [ 1 ]. The management of sepsis requires comprehensive care, emphasizing early identification and intervention for infections, hemodynamic instability, and multi-organ dysfunction [ 4 ]. Clinical assessment tools facilitate timely diagnosis, including the Sequential Organ Failure Assessment (SOFA), Quick qSOFA [ 5 ], Systemic Inflammatory Response Syndrome (SIRS) criteria, and National Early Warning Score (NEWS) [ 6 , 7 ] for risk stratification [ 5 , 8 ]. The condition’s time-sensitive nature requires prompt clinical intervention to minimize mortality and morbidity [ 3 , 9 ]. Hence, contemporary guidelines emphasize standardized protocols for early management, including the Surviving Sepsis Campaign (SSC) 1-hour bundle [ 10 , 11 ], which requires risk determination, lactate measurement, blood culture collection, broad-spectrum antibiotic administration, and crystalloid resuscitation for hypotension or hyperlactatemia [ 12 , 13 ], alongside the “Sepsis Six” strategy incorporating urine output monitoring [ 14 ]. Substantial evidence confirms that rapid recognition and adherence to these evidence-based interventions significantly reduce mortality and improve clinical outcomes in sepsis and septic shock [ 2 , 3 , 15 , 16 ]. As frontline responders, emergency department (ED) nurses serve a critical function in early sepsis recognition and standardized protocol implementation [ 1 , 2 ]. High-quality care delivery enhances early sepsis detection rates and timely intervention execution [ 13 ], reducing adverse events and improving patient outcomes [ 17 ]. Consequently, identifying reliable and sensitive healthcare quality and safety indicators remains essential for healthcare system improvement [ 18 ]. The Nursing Sensitive Quality Indicator (NSQI) is an essential instrument for care quality assessment [ 19 , 20 ]. It is a surrogate measurement reflecting the quality and performance of nursing care, encompassing principles, procedures, and scales to quantify care outcomes influenced by nursing structures, processes, and outcomes [ 21 ]. Based on Donabedian’s structure-process-outcome model [ 22 , 23 ], NSQI has evolved as a critical tool for evaluating nursing quality, with standardized data collection systems established in high-income countries (e.g., the United States of America and Australia) to guide quality improvement initiatives [ 24 , 25 ]. While China initiated NSQI development later than Western counterparts, recent studies demonstrate growing research interest in adapting NSQIs to local contexts [ 19 , 26 ]. However, a significant gap persists: no standardized NSQI specific to sepsis care has been established in China, which limits the ability to evaluate the impact of nursing on sepsis outcomes. Therefore, this study aimed to address this gap by developing and validating NSQIs tailored to sepsis management in Chinese clinical settings. The research ultimately aimed to develop a context-adaptive instrument for measuring and improving care quality while supporting policy reform. Methods This study employed a constructivist paradigm, emphasizing the co-construction of knowledge through interactions with clinical experts and evidence synthesis, to address the gap in sepsis-specific NSQI within the Chinese healthcare context. The methodological framework was based on a sequential mixed-methods approach, incorporating qualitative exploration and quantitative consensus-building techniques. Step 1: Literature Review A systematic literature search was conducted across four databases: EBSCOhost, Cochrane, PubMed, and China National Knowledge Infrastructure (CNKI). The search spanned from 2014 to 2024, utilizing MeSH terms (“sepsis,” “nursing,” and “quality indicators”) and free-text keywords connected by Boolean operators. The search aimed to identify studies involving registered nurses or nurse leadership teams focusing on sepsis and quality of care. The inclusion criteria specified that the articles had to be closely related to sepsis care, full-text available, peer-reviewed, published in English, and within the specified timeframe. Articles were excluded if they involved non-nursing staff, were conference abstracts, were not in English, were not peer-reviewed, or were inaccessible (Fig. 1 ). Two trained researchers independently conducted this process, with a third researcher serving as an arbitrator for any disagreements. Step 2: Qualitative phase and semi-structured interviews The second phase consisted of semi-structured interviews with clinical experts specializing in sepsis care from emergency departments and the intensive care units (ICUs) of tertiary hospitals. Guided by the Symptom Management Model [ 27 ], the interviews explored participants’ perspectives on NSQI for sepsis and the application of symptom management principles in clinical practice. Key topics encompassed symptom recognition (e.g., early signs such as altered mental status, hypothermia, or hyperlactatemia), utilization of screening tools (e.g., qSOFA/SOFA and NEWS), challenges in atypical symptom presentation, critical nursing interventions (e.g., timely antibiotic administration, and fluid resuscitation), interdisciplinary collaboration barriers, and outcome evaluation metrics (e.g., symptom resolution time and mortality reduction). Interviews underwent audio recording, verbatim transcription, and thematic analysis to identify themes related to structural, procedural, and outcome dimensions of sepsis care [ 28 ]. These findings informed the initial draft of sepsis-specific NSQIs, subsequently refined through the Delphi consensus process. Step 3: Delphi phase, consensus building, and interaction analysis Building upon the literature review and qualitative interview findings, the third phase implemented a modified Delphi method to transform emerging themes into consensus-based priorities [ 29 ]. A multidisciplinary panel of 16 experts participated in iterative, anonymous questionnaires to evaluate the importance, rationality of formulation, and operational feasibility of candidate NSQIs for sepsis care. Each round incorporated quantitative ratings (using a 5-point Likert scale) and qualitative feedback to refine the indicators [ 30 ]. The Delphi process maintained anonymity to minimize hierarchical bias and ensure equitable participation [ 31 ]. Indicators not meeting consensus thresholds underwent revision or exclusion, yielding a validated set of sepsis-specific NSQIs aligned with clinical priorities and measurable outcomes. Setting and sample The study employed purposive sampling across two phases. For the semi-structured interviews, five clinical experts (three nurses and two physicians) with extensive sepsis care experience were recruited from the EDs and ICUs of tertiary hospitals in China. The selection criteria were 1) expertise in sepsis management, 2) clinical roles, and 3) familiarity with nursing-sensitive quality indicators. The Delphi consensus phase involved a multidisciplinary panel of 16 experts from three Chinese provinces, comprising 10 nursing specialists and six medical experts. Delphi panelists represented diverse healthcare settings, including academic medical centers and regional hospitals, ensuring representation of varied clinical practices and institutional contexts. Data analysis The qualitative data from the semi-structured interviews underwent analysis using reflexive thematic analysis (RTA) [ 28 ]. This iterative process emphasizes dynamic engagement among researchers, data, and theoretical frameworks. Following RTA’s six-phase approach, audio recordings underwent verbatim transcription and systematic analysis through repeated immersion in the transcripts to generate codes, construct themes, and refine interpretations [ 32 ]. Codes derived from participants’ descriptions of clinical challenges were mapped to the Symptom Management Model domains. Potential themes underwent critical review against raw data and contextualization within sepsis care dimensions (structural, procedural, and outcome), with reflexive discussions resolving ambiguities. The final themes informed the development of sepsis-specific NSQIs. The Delphi method was employed to develop sensitive indicators for evaluating the quality of sepsis care. A questionnaire was distributed to field experts, and the data analysis was conducted using Excel 2019 (Microsoft, Redmond, WA, USA) and SPSS 26.0 (IBM, Armonk, NY, USA). The expert questionnaire evaluated each index based on importance, applicability, and operational feasibility using a 5-point scale, with higher scores indicating greater expert recognition. Three criteria were established for entry retention: a mean score of 3.50 or higher, a full score ratio exceeding 20%, and a coefficient of variation below 0.25. Meeting all three criteria indicated strong expert consensus and recognition for that entry, confirming its significance within the system. The coefficient of experts’ judgmental basis quantifies the degree to which an expert’s opinions are grounded in objective and substantive bases, such as theoretical analysis, practical experience, reference to literature, or subjective judgment. It is part of the authority calculation and reflects the rigor or depth of each expert’s decision-making foundation. The coefficient of experts’ familiarity measures an expert’s self-reported familiarity with the specific subject matter of the study and is typically rated on a scale ranging from very unfamiliar (low value) to very familiar (high value). It directly assesses the extent of each expert’s knowledge regarding the research topic. The coefficient of experts’ authority combines the coefficients of judgmental basis and familiarity to provide an overall index of an expert’s authority in the Delphi process. The analytical hierarchy process (AHP) was used to determine indicator weights by structuring a decision problem into a hierarchy, comparing criteria pairwise using a scale to establish their relative importance, and then synthesizing these judgments into numerical weights. Key steps included creating a pairwise comparison matrix, normalizing it to find the relative importance of each criterion, calculating the weights from the normalized matrix, and checking for matrix consistency to ensure reliable results. The weighting of indicators was determined through a systematic, two-stage analytical process. First, during the Delphi consultation rounds, panelists rated each candidate indicator on importance using a 5-point Likert scale. Second, the relative weights for primary, secondary, and tertiary indicators were calculated using the analytic hierarchy process (AHP), based on the aggregated expert ratings of importance. We utilized the average importance scores for each indicator, derived from the second round of Delphi expert consultation involving 16 experts. These mean importance scores of indicators within the same hierarchy were compared pairwise by subtraction. Based on the interval in which the difference values fell, the corresponding Saaty scale values were determined, which were then used to construct the judgment matrix (or pairwise comparison matrix).Using EXCEL 2019 software, the Analytical Hierarchy Process AHP and the multiplication method (or geometric mean method) were applied to calculate the weights and the maximum eigenvalue of the judgment matrix, followed by a consistency check. The weights for the Level 1, Level 2, and Level 3 indicators were calculated sequentially. The combined weight of a Level 2 indicator was obtained by multiplying its Level 1 indicator weight by its Level 2 indicator weight. The final combined weight of a Level 3 indicator was calculated as the product of its Level 3 indicator weight and the combined weight of its corresponding Level 2 indicator, thus yielding the weights for the entire indicator system. Results Following Donabedian’s “Structure-Process-Outcome” model and symptom management theory, a three-tiered framework for sepsis nursing care quality indicators was established through a systematic literature review (encompassing 27 studies, following the PRISMA 2020 guidelines) ( Figure 1 ) and thematic analysis of semi-structured interviews. The review identified key quality-sensitive domains, while interviews with frontline clinicians revealed three interconnected dimensions: structural, procedural, and outcome-related. The interviews also uncovered implementation barriers, including delayed recognition of atypical symptoms and inconsistent 1-hour bundle execution, alongside facilitators such as protocol standardization and real-time decision support systems. These insights were synthesized to refine the preliminary framework into a Delphi consultation scale comprising three domains, seven categories, and 23 evidence-based indicators, ensuring coherence between empirical evidence and clinical practice. During the Delphi phase, a team of experienced medical and nursing specialists was assembled from five hospitals across three Chinese provinces. The group included 16 experts: 10 nursing professionals and six medical specialists. Two rounds of Delphi consultation were conducted to evaluate the importance, applicability, and operational feasibility of sepsis care sensitivity indicators, leading to a consensus. In this study, 16 questionnaires were distributed to relevant field experts during both consultation rounds. Table 1 presents the basic information about the experts. All 16 questionnaires were valid, achieving a 100% validity rate for both rounds ( Table 2 ). In the first round, the coefficient of experts’ judgmental basis (Ca) was 0.98, with the coefficient of experts’ familiarity (Cs) at 0.92, yielding an authority coefficient of 0.95. In the second round, the Ca remained at 0.98, while the Cs increased to 0.94, resulting in an authority coefficient of 0.96 ( Table 3 ). The level of consensus of the expert opinion was measured using Kendall’s W coefficient to assess inter-expert consistency. The first-round analysis of 16 experts’ indicator scores yielded Kendall’s coefficient of concordance (W) values of 0.120, 0.316, and 0.146 for applicability, importance, and operability, respectively, all achieving P<0.05. The second round produced Kendall’s coefficient values of 0.116, 0.125, and 0.142, maintaining significance at P<0.05 ( Table 4 ). While they were statistically significant (all P<0.05), low W coefficients indicate a low level of agreement among the experts. First-round Delphi results The initial evaluation of primary indicators demonstrated expert consensus validity. Applicability scores ranged from 4.81 to 4.88 (CV=0.07-0.11; full score rate=87.50%). Importance scores reflected a consensus (mean = 4.81-5.00, CV = 0.00-0.11) with full score rates between 87.50% and 100%. Operational feasibility scores matched appropriateness metrics (mean = 4.81-4.88, CV = 0.10-0.11; full score rate = 87.50%-93.75%). All primary indicators satisfied the inclusion criteria and were maintained without modification ( Table 5 ). Consequently, primary indicators did not require a second round of expert consultation. Secondary indicators achieved consensus thresholds for appropriateness (mean=4.75-4.94, CV=0.05-0.14; full score rate=87.50%-93.75%) and operational feasibility (mean=4.63-4.94, CV=0.05-0.16; full score rate=68.75%-93.75%). Importance scores demonstrated greater variation (mean=3.88- 4.94, CV=0.05- 0.23), though 93.75% of items maintained full score rates of ≥31.25% ( Table 6 ). Despite consensus, experts recommended essential revisions to enhance clinical alignment: 1) modifying “1-hour bundle management” to “Early Intervention”; 2) distinguishing non-invasive versus invasive monitoring in nursing surveillance protocols according to WHO risk stratification standards; 3) incorporating multidisciplinary collaboration metrics across structural (e.g., interprofessional team coordination) and process domains (e.g., collaborative workflows). These significant modifications required second-round validation. Among tertiary indicators, three items were excluded due to excessive variability (CV>0.25): “SIRS assessment compliance” (2.1.1, all CV>0.25), “nursing documentation accuracy” (3.2.1, CV=0.29/0.28/0.26), and “health education awareness” (3.2.2, CV=0.26-0.29; importance full score rate=18.75%) ( Table 7 ). The retained items underwent evidence-based revisions, including: 1) standardized terminology alignment with current guidelines; 2) replacement of SIRS with qSOFA/SOFA prioritization; 3) expanded pathogen detection protocols; 4) implementation of restrictive fluid resuscitation with vasopressor protocols and fluid responsiveness testing; 5) addition of risk-adjusted 28-day sepsis mortality metrics to isolate non-nursing confounders. Multidisciplinary collaboration elements and nursing-sensitive outcome measures were systematically integrated. Following study group discussions, the expert modifications from the first round underwent revision, and adjusted indicators proceeded to second-round expert review and analysis. Second-round Delphi results The second Delphi round validated the finalized sepsis care-sensitive quality indicator system, comprising three primary, nine secondary, and 30 tertiary indicators. Secondary indicators demonstrated consensus across all domains, with appropriateness scores ranging from 3.94 to 4.75 (coefficient of variation [CV] = 0.12-0.24; full score rate = 37.50%-81.25%). Importance ratings showed stronger agreement (mean = 4.25-4.75; CV = 0.07-0.16; full score rate = 37.50%-75.00%), while operational feasibility scores remained consistent (mean = 4.25-4.69; CV = 0.13-0.16; full score rate = 37.50%-75.00%). All secondary indicators met the predefined inclusion criteria without further revisions ( Table 8 ). For tertiary indicators, substantial consensus emerged: appropriateness (mean = 4.13-4.88; CV = 0.07-0.20; full score rate = 31.25%-87.50%), importance (mean = 4.06-4.75; CV = 0.09-0.20; full score rate = 31.25%-75.00%), and operational feasibility (mean = 4.13-4.94; CV = 0.05-0.20; full score rate = 31.25%-93.75%). No tertiary indicators required elimination or modification, confirming their clinical relevance and measurability ( Table 9 ). Experts proposed no additional revisions in this round, demonstrating the framework’s stability. The finalized system established a hierarchical structure comprising three primary domains, nine secondary domains, and thirty actionable tertiary indicators, creating a validated tool for assessing nursing care quality in sepsis management ( Table 10 ). The weights and combination weights for the sepsis care quality sensitive indicator evaluation system are detailed in Table 11 . This two-round Delphi process achieved expert consensus, confirming the system’s alignment with clinical guidelines and feasibility. Discussion The creation of care-sensitive quality indicators is a vital aspect of care management. Currently, China lacks a comprehensive, objective, and sensitive framework for evaluating the quality of sepsis care. To address this need, the present study developed and validated a set of NSQIs specifically designed for sepsis management in Chinese clinical practice. The findings indicate that these indicators reflect three core dimensions of sepsis care quality: importance, rationality, and operational feasibility. Through a two-round Delphi process, expert consensus was achieved, ensuring that the indicators were consistent with clinical guidelines and applicable in real-world practice. This study thus provides an important framework for evaluating the quality of sepsis care delivery. This Delphi study developed a three-tiered sepsis care quality indicator system, consisting of three primary, nine secondary, and 30 tertiary indicators, based on Donabedian’s Structure-Process-Outcome framework [ 22 , 23 ]. The hierarchical weight distribution (process indicators (weight = 0.491) > structural indicators (0.312) > outcome indicators (0.197)) reflects the time-sensitive nature of sepsis management, where frontline clinical interventions supersede structural prerequisites in determining patient outcomes. It corresponds with evidence linking hourly delays in sepsis treatment to a 7.6% increase in mortality, emphasizing the critical role of nurses in rapid decision-making [ 14 , 33 ]. Within structural domains, the secondary indicator hierarchy (knowledge-skill training > human resources > multidisciplinary configuration) revealed significant gaps in sepsis education and workforce optimization. The highest-weighted tertiary structural indicator, “1.2.1 Theoretical knowledge compliance rate” (weight = 0.1147), highlights the perceived importance of standardized training programs, which increased 1-hour bundle adherence from 29% to 63% [ 34 ]. However, three key challenges may emerge. (1) Competency deteriorates at a rate of 31% per 6 months without refresher training [ 35 ], necessitating microlearning interventions. (2) Advanced practice nurses with ≥ 10 years of experience exhibited superior knowledge of sepsis and improved recognition and intervention rates [ 36 , 37 ]. Yet, their reluctance to adopt evidence-based strategies indicates a tension between experience and adaptability [ 38 ]. (3) Although healthcare organizations meet basic requirements for multidisciplinary teams, few achieve effective operational integration [ 13 ]. Evidence suggests that meeting administrative compliance standards alone fails to ensure quality care coordination [ 39 ]. Among the optimized process indicators, monitoring technology’s weight (0.2486) emphasized its pivotal role in sepsis management, with its tertiary indicator “implementation rate of non-invasive vital signs monitoring” ranking second, aligning with WHO’s advocacy for non-invasive technology [ 40 ] and the “golden hour” intervention principle [ 10 , 41 ]. However, this weighting may oversimplify the complexities of sepsis management and overlook the risks associated with technology dependence. While standardized vital sign monitoring reduces ICU transfers [ 13 ], weightings might mask resource disparities, such as “inflated implementation rates” due to limited invasive monitoring availability in primary care facilities [ 42 ]. Furthermore, emphasizing monitoring implementation rates over data interpretation and clinical response may diminish nurses’ decision-making role [ 7 ]. The weight difference between early intervention and early identification (0.1108 vs. 0.0797) reflects operational feasibility priorities. However, the low weighting of venous access contrasts with its critical role in “golden hour” interventions, despite evidence supporting expanded venous access team functions [ 13 ]. Early identification tools [ 5 ], including qSOFA, SIRS criteria, and NEWS, are widely recommended for rapid screening [ 6 , 7 ]. While qSOFA/SAFA was selected based on literature and expert consensus [ 5 ], its sensitivity remains debatable compared with NEWS and SIRS [ 42 ], potentially limiting early recognition, particularly in non-ICU settings [ 6 ]. Notably, despite international guidelines increasingly favoring NEWS over qSOFA for sepsis screening, our Delphi panelists—reflecting the current clinical landscape in China—consistently endorsed qSOFA/SOFA as a familiar and pragmatic tool in resource-variable settings. This divergence highlights a critical tension between evolving global evidence and entrenched local practices, underscoring the need for context-sensitive implementation strategies that balance ideal standards with on-the-ground feasibility. The relatively low weighting of multidisciplinary collaborative processes (0.0513) contradicts their perceived value, indicating a metrics system bias toward technical procedures over teamwork. The literature indicates that multidisciplinary collaboration effectively reduces antibiotic misuse and accelerates diagnosis [ 13 , 20 ]. The limited representation of nursing specialists on the expert panel may have diminished their influence in multidisciplinary decision-making processes [ 43 ]. Within the sepsis care quality framework, outcome indicators encompass clinical outcomes and care quality dimensions. The tertiary indicator “3.2.1 Nursing satisfaction rate” (weight = 0.0799) emerged as predominant, challenging traditional assumptions about patient experience metrics lacking clinical relevance by supporting a direct correlation between nurse engagement and protocol adherence. The second-ranked indicator, “3.2.1 Incidence of catheter-related bloodstream infections” (weight = 0.0441), reflects established care bundles. This weighting highlights that standardized insertion and maintenance protocols have reduced variability across high-performing institutions [ 33 ]. The Delphi panel discussions revealed professional perspectives: nursing experts noted that this metric encompasses medical factors, including antibiotic stewardship, while physicians emphasized nursing’s essential role in aseptic compliance. This divergence highlights the importance of interdisciplinary ownership of infection control metrics. Paradoxically, “3.1.2 Risk-adjusted 28-day mortality” (weight = 0.0081) received low scores despite methodological improvements to isolate nursing-sensitive outcomes. The risk-adjustment model incorporated disease severity (APACHE III), comorbidities (Charlson Index), and resource utilization variables; however, confounding factors remain. Nursing panelists maintained that medical decisions, such as vasopressor titration, significantly influence mortality. Conversely, physicians emphasized nursing’s crucial role in detecting early deterioration, a key determinant of survival in sepsis [ 44 ]. Limitations While this study presents a validated framework for evaluating sepsis care quality, several limitations merit consideration. First, the Delphi panel consisted exclusively of experts from tertiary hospitals in China, potentially limiting the generalizability of the findings to resource-constrained settings, such as community hospitals. Considering that primary and secondary hospitals are first-line hospitals where cases of sepsis are often admitted, not including such hospitals can limit the framework’s generalizability. Second, excluding primary care clinicians and nurses introduces selection bias, as their perspectives on care barriers remain unexplored. Third, the constructivist paradigm inherently favors expert consensus over predictive validity testing, necessitating future empirical validation of the relationships between indicators and outcomes. Fourth, although they were statistically significant, the W values were low (0.116–0.316), indicating a low level of agreement among experts. Findings with a low Kendall’s W should be interpreted as preliminary or descriptive, rather than authoritative guidance. These results indicate that a strong collective agreement was not reached, limiting the strength of any recommendation. A low W may prompt the need for additional Delphi rounds aimed at clarifying questions, sharing rationales, or focusing the discussion to achieve higher consensus if operationally necessary. Such results might reflect genuine diversity in professional experience, knowledge, or perspective among experts, or ambiguity in the items/process itself. Low consensus could point to issues with clarity of the questions, heterogeneity in the panel, or insufficient information provided to participants. Finally, cultural and systemic differences in healthcare delivery may restrict the direct applicability of these NSQIs to non-Chinese contexts, particularly in settings with different nursing scopes of practice or infection control protocols. Conclusion This study established China’s first sepsis-specific NSQI system through a comprehensive mixed-methods approach, combining Donabedian’s framework with frontline clinical insights. The hierarchical system (three primary, nine secondary, and 30 tertiary indicators) prioritizes process-driven interventions, highlighting nurses’ vital role in time-sensitive sepsis management. Future research should validate these indicators across diverse healthcare settings, examine their predictive validity for patient outcomes, and adapt the framework through cross-cultural comparisons. This tool can standardize sepsis care quality assessments and inform policy reforms in China and comparable healthcare systems by connecting evidence-based guidelines with measurable nursing contributions. Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Shanghai JiaoTong University School of Medicine, Ruijin Hospital Ethics Committee, with the approval number: (2021) Ethics Approval No. 59. Since this research involved expert consultations solely for the purpose of indicator development and did not involve human subjects, patient data collection, or biological samples, the need for written informed consent to participate was formally waived by the above-mentioned IRB. Consent for publication Not Applicable Availability of data and materials Yes. I used or generated research data in this study. Competing Interests No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding This study was supported by the 2023 Shanghai Jiao Tong University School of Medicine: Nursing Development Program. Authors' contributions Chen Huang: Conceptualization; Formal analysis; Writing - Original Draft; Writing - Review & Editing. Huan Liu: data curation. Wenwen Qi: Methodology and Software. Shuyuan Zhao: Data Curation and Data Validation. Jinghua Xu: Visualisation. Feng Jing（corresponding author): Supervision and Project Administration. Erzhen Chen (corresponding author): funding acquisition. Huan Liu and Chen Huang made equal contributions; therefore, Chen Huang is the first author of this article, and Huan Liu is the co-first author of this article. Acknowledgements The authors are very grateful to the following experts for their professional insights provided during the indicator development process: Qiuying Gu Ruijin Hospital, Shanghai Jiaotong University School of Medicine Weiqin Zhang Ruijin Hospital, Shanghai Jiaotong University School of Medicine Weisong Jiang Ruijin Hospital, Shanghai Jiaotong University School of Medicine Yihui Wang Ruijin Hospital, Shanghai Jiaotong University School of Medicine Bing Zhao Ruijin Hospital, Shanghai Jiaotong University School of Medicine Ying Chen Ruijin Hospital, Shanghai Jiaotong University School of Medicine Yuhua Zhou Ruijin Hospital, Shanghai Jiaotong University School of Medicine Huaye Liu Huashan Hospital, Fudan University Jianna Zhang West China Hospital, Sichuan University Fan Li Beijing Union Medical College Hospital References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama. 2016;315(8):801-10. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med. 2021;49(11):e1063-e143. Young YM, Hamilton V, Bedding M, Horgan S, Doyle C, Cliffe K, et al. Sepsis Awareness - An Irish Survey. International Journal of Integrated Care. 2017. Paudel R, Lessard S, Jaekel C, Albrecht P, Forati AM, Heiderscheit C. Enhancing Sepsis Outcomes: A 7-Year Multidisciplinary Endeavor. Am J Med Qual. 2024;39(4):145-53. Chae BR, Kim YJ, Lee YS. Prognostic accuracy of the sequential organ failure assessment (SOFA) and quick SOFA for mortality in cancer patients with sepsis defined by systemic inflammatory response syndrome (SIRS). Support Care Cancer. 2020;28(2):653-9. Waligora G, Gaddis G, Church A, Mills L. Rapid Systematic Review: The Appropriate Use of Quick Sequential Organ Failure Assessment (qSOFA) in the Emergency Department. J Emerg Med. 2020;59(6):977-83. Algarni AM, Alfaifi MS, Al Bshabshe AA, Omair OM, Alsultan MA, Alzahrani HM, et al. Prognostic accuracy of qSOFA score, SIRS criteria, and EWSs for in-hospital mortality among adult patients presenting with suspected infection to the emergency department (PASSEM) Multicenter prospective external validation cohort study protocol. PLoS One. 2024;19(1):e0281208. Myrstad M, Ihle-Hansen H, Tveita AA, Andersen EL, Nygård S, Tveit A, et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2020;28(1):66. Rahman NA, Chan CM, Zakaria MI, Jaafar MJ. Knowledge and attitude towards identification of systemic inflammatory response syndrome (SIRS) and sepsis among emergency personnel in tertiary teaching hospital. Australas Emerg Care. 2019;22(1):13-21. Rios EM, Breda KL. Time Is Survival: Continuing Education on Sepsis for Neurosurgical Critical Care Nurses. J Contin Educ Nurs. 2024;55(5):224-30. Briegel J, Möhnle P. [Surviving Sepsis Campaign update 2018: the 1 h bundle : Background to the new recommendations]. Anaesthesist. 2019;68(4):204-7. Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Intensive Care Med. 2018;44(6):925-8. Gripp L, Raffoul M, Milner KA. Implementation of the Surviving Sepsis Campaign one-hour bundle in a short stay unit: A quality improvement project. Intensive Crit Care Nurs. 2021;63:103004. Kumar P, Jordan M, Caesar J, Miller S. Improving the management of sepsis in a district general hospital by implementing the ‘Sepsis Six’ recommendations. BMJ Qual Improv Rep. 2015;4(1). Moore WR, Vermuelen A, Taylor R, Kihara D, Wahome E. Improving 3-Hour Sepsis Bundled Care Outcomes: Implementation of a Nurse-Driven Sepsis Protocol in the Emergency Department. J Emerg Nurs. 2019;45(6):690-8. Ahmed AM, Macapili E, Brenner MJ, Pandian V. Accelerating Detection and Intervention for Sepsis in Skilled Nursing Facilities Using a Sepsis Pathway. J Nurs Care Qual. 2024;39(1):67-75. Liu C, Liu Y, Tian Y, Zhang K, Hao G, Shen L, et al. Application of the PDCA cycle for standardized nursing management in sepsis bundles. BMC Anesthesiol. 2022;22(1):39. Gao JL, Liu XM, Che WF, Xin X. Construction of nursing-sensitive quality indicators for haemodialysis using Delphi method. J Clin Nurs. 2018;27(21-22):3920-30. Yang S, Huang L-H, Zhao X-H, Xing M-Y, Shao L-W, Zhang M-Y, et al. Using the Delphi method to establish nursing-sensitive quality indicators for ICU nursing in China. Research in Nursing & Health. 2019;42(1):48-60. Tevik K, Helvik AS, Stensvik GT, Nordberg MS, Nakrem S. Nursing-sensitive quality indicators for quality improvement in Norwegian nursing homes - a modified Delphi study. BMC Health Serv Res. 2023;23(1):1068. Sullivan CE, Day SW, Ivankova N, Markaki A, Patrician PA, Landier W. Establishing nursing-sensitive quality indicators for pediatric oncology: An international mixed methods Delphi study. J Nurs Scholarsh. 2023;55(1):388-400. Donabedian A. Quality assessment and assurance: unity of purpose, diversity of means. Inquiry. 1988;25(1):173-92. Donabedian A. The quality of care. How can it be assessed? Jama. 1988;260(12):1743-8. Bonfill X, Roqué M, Aller MB, Osorio D, Foradada C, Vives A, et al. Development of quality of care indicators from systematic reviews: the case of hospital delivery. Implement Sci. 2013;8:42. Burston S, Chaboyer W, Gillespie B. Nurse-sensitive indicators suitable to reflect nursing care quality: a review and discussion of issues. J Clin Nurs. 2014;23(13-14):1785-95. Shen Z, Qin W, Zhu L, Lin Y, Ling H, Zhang Y. Construction of nursing-sensitive quality indicators for cardiac catheterisation: A Delphi study and an analytic hierarchy process. J Clin Nurs. 2022;31(19-20):2821-38. Dodd M, Janson S, Facione N, Faucett J, Froelicher ES, Humphreys J, et al. Advancing the science of symptom management. J Adv Nurs. 2001;33(5):668-76. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health. 2019;11(4):589-97. Meskell P, Murphy K, Shaw DG, Casey D. Insights into the use and complexities of the Policy Delphi technique. Nurse Res. 2014;21(3):32-9. Hsu C-C, Sandford B. The Delphi Technique: Making Sense Of Consensus. Practical Assessment, Research and Evaluation. 2007;12. Gnatzy T, Warth J, von der Gracht H, Darkow I-L. Validating an innovative real-time Delphi approach - A methodological comparison between real-time and conventional Delphi studies. Technological Forecasting and Social Change. 2011;78(9):1681-94. Byrne D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity. 2022;56(3):1391-412. Lasater KB, Sloane DM, McHugh MD, Cimiotti JP, Riman KA, Martin B, et al. Evaluation of hospital nurse-to-patient staffing ratios and sepsis bundles on patient outcomes. Am J Infect Control. 2021;49(7):868-73. Kleinpell R, Buchman TG, Harmon L, Nielsen M. Promoting Patient- and Family-Centered Care in the Intensive Care Unit: A Dissemination Project. AACN Adv Crit Care. 2017;28(2):155-9. DeGregoris JP, Bandong L, White T, Brennan MM. Quality Improvement to Promote Sepsis Reassessment: The Sepsis Reassessment Protocol Improvement Project (SRPIP). J Nurs Care Qual. 2023;38(2):107-13. Öztürk Birge A, Karabag Aydin A, Köroğlu Çamdeviren E. Intensive care nurses’ awareness of identification of early sepsis findings. J Clin Nurs. 2022;31(19-20):2886-99. Chua WL, Teh CS, Basri M, Ong ST, Phang NQQ, Goh EL. Nurses’ knowledge and confidence in recognizing and managing patients with sepsis: A multi-site cross-sectional study. J Adv Nurs. 2023;79(2):616-29. Alaro MG, Ashine TM, Kebede S, Hussien H, Alaro MG, Kechine Tibore T. Knowledge and Associated Factors Towards Sepsis Management Among Nurses Working in the Emergency Department of Public Hospitals in Addis Ababa. SAGE Open Nurs. 2024;10:23779608241274224. Matthaeus-Kraemer CT, Thomas-Rueddel DO, Schwarzkopf D, Rueddel H, Poidinger B, Reinhart K, et al. Crossing the handover chasm: Clinicians’ perceptions of barriers to the early detection and timely management of severe sepsis and septic shock. J Crit Care. 2016;36:85-91. World Health Organization. WHO Director-General’s opening remarks at Global Consultation (Virtual) “Partners in action: Engaging stakeholders for implementing the Global Patient Safety Action Plan 2021–2030”. Available at https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-global-consultation-virtual-partners-in-action-engaging-stakeholders-for-implementing-the-global-patient-safety-action-plan-2021-2030. Accessed September 18, 20252021. Ko BS, Choi SH, Shin TG, Kim K, Jo YH, Ryoo SM, et al. Impact of 1-Hour Bundle Achievement in Septic Shock. J Clin Med. 2021;10(3). Chua WL, Rusli KDB, Aitken LM. Early warning scores for sepsis identification and prediction of in-hospital mortality in adults with sepsis: A systematic review and meta-analysis. J Clin Nurs. 2024;33(6):2005-18. Reich EN, Then KL, Rankin JA. Barriers to Clinical Practice Guideline Implementation for Septic Patients in the Emergency Department. J Emerg Nurs. 2018;44(6):552-62. Ramos Corrêa Pinto L, Azzolin KO, Lucena AF, Moretti MMS, Haas JS, Moraes RB, et al. Septic shock: Clinical indicators and implications to critical patient care. J Clin Nurs. 2021;30(11-12):1607-14. Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev. 2022;18(2):e1230. Tables Tables 1 to 11 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor invited by journal 25 Dec, 2025 Editor assigned by journal 04 Dec, 2025 Submission checks completed at journal 04 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a life-threatening organ dysfunction caused by an uncontrolled immune response to infection. It represents a significant global health challenge, accounting for 19.7% of annual deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Common symptoms of sepsis include drowsiness, chills or fever, hypothermia, nausea, low blood pressure, and a rapid heartbeat [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with sepsis may also experience shock, multiple organ failure, decreased urine output, and acute respiratory distress, all of which can lead to death if the condition worsens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe management of sepsis requires comprehensive care, emphasizing early identification and intervention for infections, hemodynamic instability, and multi-organ dysfunction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Clinical assessment tools facilitate timely diagnosis, including the Sequential Organ Failure Assessment (SOFA), Quick qSOFA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Systemic Inflammatory Response Syndrome (SIRS) criteria, and National Early Warning Score (NEWS) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] for risk stratification [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The condition\u0026rsquo;s time-sensitive nature requires prompt clinical intervention to minimize mortality and morbidity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hence, contemporary guidelines emphasize standardized protocols for early management, including the Surviving Sepsis Campaign (SSC) 1-hour bundle [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which requires risk determination, lactate measurement, blood culture collection, broad-spectrum antibiotic administration, and crystalloid resuscitation for hypotension or hyperlactatemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], alongside the \u0026ldquo;Sepsis Six\u0026rdquo; strategy incorporating urine output monitoring [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Substantial evidence confirms that rapid recognition and adherence to these evidence-based interventions significantly reduce mortality and improve clinical outcomes in sepsis and septic shock [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs frontline responders, emergency department (ED) nurses serve a critical function in early sepsis recognition and standardized protocol implementation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. High-quality care delivery enhances early sepsis detection rates and timely intervention execution [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], reducing adverse events and improving patient outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, identifying reliable and sensitive healthcare quality and safety indicators remains essential for healthcare system improvement [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Nursing Sensitive Quality Indicator (NSQI) is an essential instrument for care quality assessment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is a surrogate measurement reflecting the quality and performance of nursing care, encompassing principles, procedures, and scales to quantify care outcomes influenced by nursing structures, processes, and outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on Donabedian\u0026rsquo;s structure-process-outcome model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], NSQI has evolved as a critical tool for evaluating nursing quality, with standardized data collection systems established in high-income countries (e.g., the United States of America and Australia) to guide quality improvement initiatives [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While China initiated NSQI development later than Western counterparts, recent studies demonstrate growing research interest in adapting NSQIs to local contexts [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, a significant gap persists: no standardized NSQI specific to sepsis care has been established in China, which limits the ability to evaluate the impact of nursing on sepsis outcomes.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to address this gap by developing and validating NSQIs tailored to sepsis management in Chinese clinical settings. The research ultimately aimed to develop a context-adaptive instrument for measuring and improving care quality while supporting policy reform.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a constructivist paradigm, emphasizing the co-construction of knowledge through interactions with clinical experts and evidence synthesis, to address the gap in sepsis-specific NSQI within the Chinese healthcare context. The methodological framework was based on a sequential mixed-methods approach, incorporating qualitative exploration and quantitative consensus-building techniques.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStep 1: Literature Review\u003c/h2\u003e \u003cp\u003eA systematic literature search was conducted across four databases: EBSCOhost, Cochrane, PubMed, and China National Knowledge Infrastructure (CNKI). The search spanned from 2014 to 2024, utilizing MeSH terms (\u0026ldquo;sepsis,\u0026rdquo; \u0026ldquo;nursing,\u0026rdquo; and \u0026ldquo;quality indicators\u0026rdquo;) and free-text keywords connected by Boolean operators. The search aimed to identify studies involving registered nurses or nurse leadership teams focusing on sepsis and quality of care. The inclusion criteria specified that the articles had to be closely related to sepsis care, full-text available, peer-reviewed, published in English, and within the specified timeframe. Articles were excluded if they involved non-nursing staff, were conference abstracts, were not in English, were not peer-reviewed, or were inaccessible (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two trained researchers independently conducted this process, with a third researcher serving as an arbitrator for any disagreements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStep 2: Qualitative phase and semi-structured interviews\u003c/h3\u003e\n\u003cp\u003eThe second phase consisted of semi-structured interviews with clinical experts specializing in sepsis care from emergency departments and the intensive care units (ICUs) of tertiary hospitals. Guided by the Symptom Management Model [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the interviews explored participants\u0026rsquo; perspectives on NSQI for sepsis and the application of symptom management principles in clinical practice. Key topics encompassed symptom recognition (e.g., early signs such as altered mental status, hypothermia, or hyperlactatemia), utilization of screening tools (e.g., qSOFA/SOFA and NEWS), challenges in atypical symptom presentation, critical nursing interventions (e.g., timely antibiotic administration, and fluid resuscitation), interdisciplinary collaboration barriers, and outcome evaluation metrics (e.g., symptom resolution time and mortality reduction). Interviews underwent audio recording, verbatim transcription, and thematic analysis to identify themes related to structural, procedural, and outcome dimensions of sepsis care [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings informed the initial draft of sepsis-specific NSQIs, subsequently refined through the Delphi consensus process.\u003c/p\u003e\n\u003ch3\u003eStep 3: Delphi phase, consensus building, and interaction analysis\u003c/h3\u003e\n\u003cp\u003eBuilding upon the literature review and qualitative interview findings, the third phase implemented a modified Delphi method to transform emerging themes into consensus-based priorities [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A multidisciplinary panel of 16 experts participated in iterative, anonymous questionnaires to evaluate the importance, rationality of formulation, and operational feasibility of candidate NSQIs for sepsis care. Each round incorporated quantitative ratings (using a 5-point Likert scale) and qualitative feedback to refine the indicators [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The Delphi process maintained anonymity to minimize hierarchical bias and ensure equitable participation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Indicators not meeting consensus thresholds underwent revision or exclusion, yielding a validated set of sepsis-specific NSQIs aligned with clinical priorities and measurable outcomes.\u003c/p\u003e\n\u003ch3\u003eSetting and sample\u003c/h3\u003e\n\u003cp\u003eThe study employed purposive sampling across two phases. For the semi-structured interviews, five clinical experts (three nurses and two physicians) with extensive sepsis care experience were recruited from the EDs and ICUs of tertiary hospitals in China. The selection criteria were 1) expertise in sepsis management, 2) clinical roles, and 3) familiarity with nursing-sensitive quality indicators. The Delphi consensus phase involved a multidisciplinary panel of 16 experts from three Chinese provinces, comprising 10 nursing specialists and six medical experts. Delphi panelists represented diverse healthcare settings, including academic medical centers and regional hospitals, ensuring representation of varied clinical practices and institutional contexts.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe qualitative data from the semi-structured interviews underwent analysis using reflexive thematic analysis (RTA) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This iterative process emphasizes dynamic engagement among researchers, data, and theoretical frameworks. Following RTA\u0026rsquo;s six-phase approach, audio recordings underwent verbatim transcription and systematic analysis through repeated immersion in the transcripts to generate codes, construct themes, and refine interpretations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Codes derived from participants\u0026rsquo; descriptions of clinical challenges were mapped to the Symptom Management Model domains. Potential themes underwent critical review against raw data and contextualization within sepsis care dimensions (structural, procedural, and outcome), with reflexive discussions resolving ambiguities. The final themes informed the development of sepsis-specific NSQIs.\u003c/p\u003e \u003cp\u003eThe Delphi method was employed to develop sensitive indicators for evaluating the quality of sepsis care. A questionnaire was distributed to field experts, and the data analysis was conducted using Excel 2019 (Microsoft, Redmond, WA, USA) and SPSS 26.0 (IBM, Armonk, NY, USA). The expert questionnaire evaluated each index based on importance, applicability, and operational feasibility using a 5-point scale, with higher scores indicating greater expert recognition. Three criteria were established for entry retention: a mean score of 3.50 or higher, a full score ratio exceeding 20%, and a coefficient of variation below 0.25. Meeting all three criteria indicated strong expert consensus and recognition for that entry, confirming its significance within the system.\u003c/p\u003e \u003cp\u003eThe coefficient of experts\u0026rsquo; judgmental basis quantifies the degree to which an expert\u0026rsquo;s opinions are grounded in objective and substantive bases, such as theoretical analysis, practical experience, reference to literature, or subjective judgment. It is part of the authority calculation and reflects the rigor or depth of each expert\u0026rsquo;s decision-making foundation. The coefficient of experts\u0026rsquo; familiarity measures an expert\u0026rsquo;s self-reported familiarity with the specific subject matter of the study and is typically rated on a scale ranging from very unfamiliar (low value) to very familiar (high value). It directly assesses the extent of each expert\u0026rsquo;s knowledge regarding the research topic. The coefficient of experts\u0026rsquo; authority combines the coefficients of judgmental basis and familiarity to provide an overall index of an expert\u0026rsquo;s authority in the Delphi process. The analytical hierarchy process (AHP) was used to determine indicator weights by structuring a decision problem into a hierarchy, comparing criteria pairwise using a scale to establish their relative importance, and then synthesizing these judgments into numerical weights. Key steps included creating a pairwise comparison matrix, normalizing it to find the relative importance of each criterion, calculating the weights from the normalized matrix, and checking for matrix consistency to ensure reliable results.\u003c/p\u003e \u003cp\u003eThe weighting of indicators was determined through a systematic, two-stage analytical process. First, during the Delphi consultation rounds, panelists rated each candidate indicator on importance using a 5-point Likert scale. Second, the relative weights for primary, secondary, and tertiary indicators were calculated using the analytic hierarchy process (AHP), based on the aggregated expert ratings of importance.\u003c/p\u003e \u003cp\u003eWe utilized the average importance scores for each indicator, derived from the second round of Delphi expert consultation involving 16 experts. These mean importance scores of indicators within the same hierarchy were compared pairwise by subtraction. Based on the interval in which the difference values fell, the corresponding Saaty scale values were determined, which were then used to construct the judgment matrix (or pairwise comparison matrix).Using EXCEL 2019 software, the Analytical Hierarchy Process AHP and the multiplication method (or geometric mean method) were applied to calculate the weights and the maximum eigenvalue of the judgment matrix, followed by a consistency check. The weights for the Level 1, Level 2, and Level 3 indicators were calculated sequentially. The combined weight of a Level 2 indicator was obtained by multiplying its Level 1 indicator weight by its Level 2 indicator weight. The final combined weight of a Level 3 indicator was calculated as the product of its Level 3 indicator weight and the combined weight of its corresponding Level 2 indicator, thus yielding the weights for the entire indicator system.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFollowing Donabedian’s “Structure-Process-Outcome” model and symptom management theory, a three-tiered framework for sepsis nursing care quality indicators was established through a systematic literature review (encompassing 27 studies, following the PRISMA 2020 guidelines) (\u003cstrong\u003eFigure 1\u003c/strong\u003e) and thematic analysis of semi-structured interviews. The review identified key quality-sensitive domains, while interviews with frontline clinicians revealed three interconnected dimensions: structural, procedural, and outcome-related. The interviews also uncovered implementation barriers, including delayed recognition of atypical symptoms and inconsistent 1-hour bundle\u0026nbsp;execution, alongside facilitators such as protocol standardization and real-time decision support systems. These insights\u0026nbsp;were synthesized to refine the preliminary framework into a Delphi consultation scale comprising three domains, seven categories, and 23 evidence-based indicators, ensuring coherence between empirical evidence and clinical practice.\u003c/p\u003e\n\u003cp\u003eDuring the Delphi phase, a team of experienced medical and nursing specialists was assembled from five hospitals across three Chinese provinces. The group included 16 experts: 10 nursing professionals and six medical specialists. Two rounds of Delphi consultation were conducted to evaluate the importance, applicability, and operational feasibility of sepsis care sensitivity indicators, leading to a consensus.\u003c/p\u003e\n\u003cp\u003eIn this study, 16 questionnaires were distributed to relevant field experts during both consultation rounds. \u003cstrong\u003eTable 1\u003c/strong\u003e presents the basic information about the experts. All 16 questionnaires were valid, achieving a 100% validity rate for both rounds (\u003cstrong\u003eTable 2\u003c/strong\u003e). In the first round, the coefficient of experts’ judgmental basis (Ca) was 0.98, with the coefficient of experts’ familiarity (Cs) at 0.92, yielding an authority coefficient of 0.95. In the second round, the Ca remained at 0.98, while the Cs increased to 0.94, resulting in an authority coefficient of 0.96 (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe level of consensus of the expert opinion was measured using Kendall’s W coefficient to assess inter-expert consistency. The first-round\u0026nbsp;analysis of 16 experts’ indicator scores yielded Kendall’s coefficient of concordance (W) values of 0.120, 0.316, and 0.146\u0026nbsp;for applicability,\u0026nbsp;importance, and operability, respectively, all achieving P\u0026lt;0.05. The second round produced Kendall’s coefficient values of 0.116, 0.125, and 0.142, maintaining significance at P\u0026lt;0.05 (\u003cstrong\u003eTable 4\u003c/strong\u003e). While they were statistically significant (all P\u0026lt;0.05), low W coefficients indicate a low level of agreement among the experts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirst-round Delphi results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial evaluation of primary indicators demonstrated expert consensus validity. Applicability scores ranged from 4.81 to 4.88 (CV=0.07-0.11; full score rate=87.50%). Importance scores reflected a consensus (mean = 4.81-5.00, CV = 0.00-0.11) with full score rates between 87.50% and 100%. Operational feasibility scores matched appropriateness metrics (mean = 4.81-4.88, CV = 0.10-0.11; full score rate = 87.50%-93.75%). All primary indicators satisfied the inclusion criteria and were maintained without modification (\u003cstrong\u003eTable 5\u003c/strong\u003e). Consequently, primary indicators did not require a second round of expert consultation.\u003c/p\u003e\n\u003cp\u003eSecondary indicators achieved consensus thresholds for appropriateness (mean=4.75-4.94, CV=0.05-0.14; full score rate=87.50%-93.75%) and operational feasibility (mean=4.63-4.94, CV=0.05-0.16; full score rate=68.75%-93.75%). Importance scores demonstrated greater variation (mean=3.88- 4.94, CV=0.05- 0.23), though 93.75% of items maintained full score rates of ≥31.25% (\u003cstrong\u003eTable 6\u003c/strong\u003e). Despite consensus, experts recommended essential revisions to enhance clinical alignment: 1) modifying “1-hour bundle management” to “Early Intervention”; 2) distinguishing non-invasive versus invasive monitoring in nursing surveillance protocols according to WHO risk stratification standards; 3) incorporating multidisciplinary collaboration metrics across structural (e.g., interprofessional team coordination) and process domains (e.g., collaborative workflows). These significant modifications required second-round validation.\u003c/p\u003e\n\u003cp\u003eAmong tertiary indicators, three items were excluded due to excessive variability (CV\u0026gt;0.25): “SIRS assessment compliance” (2.1.1, all CV\u0026gt;0.25), “nursing documentation accuracy” (3.2.1, CV=0.29/0.28/0.26), and “health education awareness” (3.2.2, CV=0.26-0.29; importance full score rate=18.75%) (\u003cstrong\u003eTable 7\u003c/strong\u003e). The retained items underwent evidence-based revisions, including: 1) standardized terminology alignment with current guidelines; 2) replacement of SIRS with qSOFA/SOFA prioritization; 3) expanded pathogen detection protocols; 4) implementation of restrictive fluid resuscitation with vasopressor protocols and fluid responsiveness testing; 5) addition of risk-adjusted 28-day sepsis mortality metrics to isolate non-nursing confounders. Multidisciplinary collaboration elements and nursing-sensitive outcome measures were systematically integrated. Following study group\u0026nbsp;discussions, the expert modifications from the first round underwent revision, and adjusted indicators proceeded to second-round expert review and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecond-round Delphi results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second Delphi round validated the finalized sepsis care-sensitive quality indicator system, comprising three primary, nine secondary, and 30 tertiary indicators. Secondary indicators demonstrated consensus across all domains, with appropriateness scores ranging from 3.94 to 4.75 (coefficient of variation [CV] = 0.12-0.24; full score rate = 37.50%-81.25%). Importance ratings showed stronger agreement (mean = 4.25-4.75; CV = 0.07-0.16; full score rate = 37.50%-75.00%), while operational feasibility scores remained consistent (mean = 4.25-4.69; CV = 0.13-0.16; full score rate = 37.50%-75.00%). All secondary indicators met the predefined inclusion criteria without further revisions (\u003cstrong\u003eTable 8\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eFor tertiary indicators, substantial consensus emerged: appropriateness (mean = 4.13-4.88; CV = 0.07-0.20; full score rate = 31.25%-87.50%), importance (mean = 4.06-4.75; CV = 0.09-0.20; full score rate = 31.25%-75.00%), and operational feasibility (mean = 4.13-4.94; CV = 0.05-0.20; full score rate = 31.25%-93.75%). No tertiary indicators required elimination or modification, confirming their clinical relevance and measurability (\u003cstrong\u003eTable 9\u003c/strong\u003e). Experts proposed no additional revisions in this round, demonstrating the framework’s stability.\u003c/p\u003e\n\u003cp\u003eThe finalized system established a hierarchical structure comprising three primary domains, nine secondary domains, and thirty actionable tertiary indicators, creating a validated tool for assessing nursing care quality in sepsis management (\u003cstrong\u003eTable 10\u003c/strong\u003e). The weights and combination weights for the sepsis care quality sensitive indicator evaluation system are detailed in \u003cstrong\u003eTable 11\u003c/strong\u003e. This two-round Delphi process achieved expert consensus, confirming the system’s alignment with clinical guidelines and feasibility.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe creation of care-sensitive quality indicators is a vital aspect of care management. Currently, China lacks a comprehensive, objective, and sensitive framework for evaluating the quality of sepsis care. To address this need, the present study developed and validated a set of NSQIs specifically designed for sepsis management in Chinese clinical practice. The findings indicate that these indicators reflect three core dimensions of sepsis care quality: importance, rationality, and operational feasibility. Through a two-round Delphi process, expert consensus was achieved, ensuring that the indicators were consistent with clinical guidelines and applicable in real-world practice. This study thus provides an important framework for evaluating the quality of sepsis care delivery.\u003c/p\u003e \u003cp\u003eThis Delphi study developed a three-tiered sepsis care quality indicator system, consisting of three primary, nine secondary, and 30 tertiary indicators, based on Donabedian\u0026rsquo;s Structure-Process-Outcome framework [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The hierarchical weight distribution (process indicators (weight\u0026thinsp;=\u0026thinsp;0.491)\u0026thinsp;\u0026gt;\u0026thinsp;structural indicators (0.312)\u0026thinsp;\u0026gt;\u0026thinsp;outcome indicators (0.197)) reflects the time-sensitive nature of sepsis management, where frontline clinical interventions supersede structural prerequisites in determining patient outcomes. It corresponds with evidence linking hourly delays in sepsis treatment to a 7.6% increase in mortality, emphasizing the critical role of nurses in rapid decision-making [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin structural domains, the secondary indicator hierarchy (knowledge-skill training\u0026thinsp;\u0026gt;\u0026thinsp;human resources\u0026thinsp;\u0026gt;\u0026thinsp;multidisciplinary configuration) revealed significant gaps in sepsis education and workforce optimization. The highest-weighted tertiary structural indicator, \u0026ldquo;1.2.1 Theoretical knowledge compliance rate\u0026rdquo; (weight\u0026thinsp;=\u0026thinsp;0.1147), highlights the perceived importance of standardized training programs, which increased 1-hour bundle adherence from 29% to 63% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, three key challenges may emerge. (1) Competency deteriorates at a rate of 31% per 6 months without refresher training [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], necessitating microlearning interventions. (2) Advanced practice nurses with \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10 years of experience exhibited superior knowledge of sepsis and improved recognition and intervention rates [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Yet, their reluctance to adopt evidence-based strategies indicates a tension between experience and adaptability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. (3) Although healthcare organizations meet basic requirements for multidisciplinary teams, few achieve effective operational integration [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Evidence suggests that meeting administrative compliance standards alone fails to ensure quality care coordination [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the optimized process indicators, monitoring technology\u0026rsquo;s weight (0.2486) emphasized its pivotal role in sepsis management, with its tertiary indicator \u0026ldquo;implementation rate of non-invasive vital signs monitoring\u0026rdquo; ranking second, aligning with WHO\u0026rsquo;s advocacy for non-invasive technology [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and the \u0026ldquo;golden hour\u0026rdquo; intervention principle [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, this weighting may oversimplify the complexities of sepsis management and overlook the risks associated with technology dependence. While standardized vital sign monitoring reduces ICU transfers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], weightings might mask resource disparities, such as \u0026ldquo;inflated implementation rates\u0026rdquo; due to limited invasive monitoring availability in primary care facilities [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, emphasizing monitoring implementation rates over data interpretation and clinical response may diminish nurses\u0026rsquo; decision-making role [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe weight difference between early intervention and early identification (0.1108 vs. 0.0797) reflects operational feasibility priorities. However, the low weighting of venous access contrasts with its critical role in \u0026ldquo;golden hour\u0026rdquo; interventions, despite evidence supporting expanded venous access team functions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Early identification tools [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], including qSOFA, SIRS criteria, and NEWS, are widely recommended for rapid screening [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While qSOFA/SAFA was selected based on literature and expert consensus [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], its sensitivity remains debatable compared with NEWS and SIRS [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], potentially limiting early recognition, particularly in non-ICU settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Notably, despite international guidelines increasingly favoring NEWS over qSOFA for sepsis screening, our Delphi panelists\u0026mdash;reflecting the current clinical landscape in China\u0026mdash;consistently endorsed qSOFA/SOFA as a familiar and pragmatic tool in resource-variable settings. This divergence highlights a critical tension between evolving global evidence and entrenched local practices, underscoring the need for context-sensitive implementation strategies that balance ideal standards with on-the-ground feasibility.\u003c/p\u003e \u003cp\u003eThe relatively low weighting of multidisciplinary collaborative processes (0.0513) contradicts their perceived value, indicating a metrics system bias toward technical procedures over teamwork. The literature indicates that multidisciplinary collaboration effectively reduces antibiotic misuse and accelerates diagnosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The limited representation of nursing specialists on the expert panel may have diminished their influence in multidisciplinary decision-making processes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin the sepsis care quality framework, outcome indicators encompass clinical outcomes and care quality dimensions. The tertiary indicator \u0026ldquo;3.2.1 Nursing satisfaction rate\u0026rdquo; (weight\u0026thinsp;=\u0026thinsp;0.0799) emerged as predominant, challenging traditional assumptions about patient experience metrics lacking clinical relevance by supporting a direct correlation between nurse engagement and protocol adherence.\u003c/p\u003e \u003cp\u003eThe second-ranked indicator, \u0026ldquo;3.2.1 Incidence of catheter-related bloodstream infections\u0026rdquo; (weight\u0026thinsp;=\u0026thinsp;0.0441), reflects established care bundles. This weighting highlights that standardized insertion and maintenance protocols have reduced variability across high-performing institutions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Delphi panel discussions revealed professional perspectives: nursing experts noted that this metric encompasses medical factors, including antibiotic stewardship, while physicians emphasized nursing\u0026rsquo;s essential role in aseptic compliance. This divergence highlights the importance of interdisciplinary ownership of infection control metrics.\u003c/p\u003e \u003cp\u003eParadoxically, \u0026ldquo;3.1.2 Risk-adjusted 28-day mortality\u0026rdquo; (weight\u0026thinsp;=\u0026thinsp;0.0081) received low scores despite methodological improvements to isolate nursing-sensitive outcomes. The risk-adjustment model incorporated disease severity (APACHE III), comorbidities (Charlson Index), and resource utilization variables; however, confounding factors remain. Nursing panelists maintained that medical decisions, such as vasopressor titration, significantly influence mortality. Conversely, physicians emphasized nursing\u0026rsquo;s crucial role in detecting early deterioration, a key determinant of survival in sepsis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study presents a validated framework for evaluating sepsis care quality, several limitations merit consideration. First, the Delphi panel consisted exclusively of experts from tertiary hospitals in China, potentially limiting the generalizability of the findings to resource-constrained settings, such as community hospitals. Considering that primary and secondary hospitals are first-line hospitals where cases of sepsis are often admitted, not including such hospitals can limit the framework\u0026rsquo;s generalizability. Second, excluding primary care clinicians and nurses introduces selection bias, as their perspectives on care barriers remain unexplored. Third, the constructivist paradigm inherently favors expert consensus over predictive validity testing, necessitating future empirical validation of the relationships between indicators and outcomes. Fourth, although they were statistically significant, the W values were low (0.116\u0026ndash;0.316), indicating a low level of agreement among experts. Findings with a low Kendall\u0026rsquo;s W should be interpreted as preliminary or descriptive, rather than authoritative guidance. These results indicate that a strong collective agreement was not reached, limiting the strength of any recommendation. A low W may prompt the need for additional Delphi rounds aimed at clarifying questions, sharing rationales, or focusing the discussion to achieve higher consensus if operationally necessary. Such results might reflect genuine diversity in professional experience, knowledge, or perspective among experts, or ambiguity in the items/process itself. Low consensus could point to issues with clarity of the questions, heterogeneity in the panel, or insufficient information provided to participants. Finally, cultural and systemic differences in healthcare delivery may restrict the direct applicability of these NSQIs to non-Chinese contexts, particularly in settings with different nursing scopes of practice or infection control protocols.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established China\u0026rsquo;s first sepsis-specific NSQI system through a comprehensive mixed-methods approach, combining Donabedian\u0026rsquo;s framework with frontline clinical insights. The hierarchical system (three primary, nine secondary, and 30 tertiary indicators) prioritizes process-driven interventions, highlighting nurses\u0026rsquo; vital role in time-sensitive sepsis management. Future research should validate these indicators across diverse healthcare settings, examine their predictive validity for patient outcomes, and adapt the framework through cross-cultural comparisons. This tool can standardize sepsis care quality assessments and inform policy reforms in China and comparable healthcare systems by connecting evidence-based guidelines with measurable nursing contributions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of Shanghai JiaoTong University School of Medicine, Ruijin Hospital Ethics Committee, with the approval number: (2021) Ethics Approval No. 59.\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Since this research involved expert consultations solely for the purpose of indicator development and did not involve human subjects, patient data collection, or biological samples, the need for written informed consent to participate was formally waived by the above-mentioned IRB.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYes. I used or generated research data in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the 2023 Shanghai Jiao Tong University School of Medicine: Nursing Development Program.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Authors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Huang: Conceptualization; Formal analysis; Writing - Original Draft; Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eHuan Liu: \u0026nbsp;data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWenwen Qi: Methodology and Software.\u003c/p\u003e\n\u003cp\u003eShuyuan Zhao: Data Curation and Data Validation.\u003c/p\u003e\n\u003cp\u003eJinghua Xu: Visualisation.\u003c/p\u003e\n\u003cp\u003eFeng Jing（corresponding author): Supervision and Project Administration.\u003c/p\u003e\n\u003cp\u003eErzhen Chen (corresponding author): funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuan Liu and Chen Huang made equal contributions; therefore, Chen Huang is the first author of this article, and Huan Liu is the co-first author of this article.\u003cbr\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are very grateful to the following experts for their professional insights provided during the indicator development process:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQiuying Gu Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eWeiqin Zhang Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eWeisong Jiang Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eYihui Wang Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eBing Zhao Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eYing Chen Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eYuhua Zhou Ruijin Hospital, Shanghai Jiaotong University School of Medicine\u003c/p\u003e\n\u003cp\u003eHuaye Liu Huashan Hospital, Fudan University\u003c/p\u003e\n\u003cp\u003eJianna Zhang West China Hospital, Sichuan University\u003c/p\u003e\n\u003cp\u003eFan Li Beijing Union Medical College Hospital\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama. 2016;315(8):801-10.\u003c/li\u003e\n \u003cli\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med. 2021;49(11):e1063-e143.\u003c/li\u003e\n \u003cli\u003eYoung YM, Hamilton V, Bedding M, Horgan S, Doyle C, Cliffe K, et al. Sepsis Awareness - An Irish Survey. International Journal of Integrated Care. 2017.\u003c/li\u003e\n \u003cli\u003ePaudel R, Lessard S, Jaekel C, Albrecht P, Forati AM, Heiderscheit C. Enhancing Sepsis Outcomes: A 7-Year Multidisciplinary Endeavor. Am J Med Qual. 2024;39(4):145-53.\u003c/li\u003e\n \u003cli\u003eChae BR, Kim YJ, Lee YS. Prognostic accuracy of the sequential organ failure assessment (SOFA) and quick SOFA for mortality in cancer patients with sepsis defined by systemic inflammatory response syndrome (SIRS). Support Care Cancer. 2020;28(2):653-9.\u003c/li\u003e\n \u003cli\u003eWaligora G, Gaddis G, Church A, Mills L. Rapid Systematic Review: The Appropriate Use of Quick Sequential Organ Failure Assessment (qSOFA) in the Emergency Department. J Emerg Med. 2020;59(6):977-83.\u003c/li\u003e\n \u003cli\u003eAlgarni AM, Alfaifi MS, Al Bshabshe AA, Omair OM, Alsultan MA, Alzahrani HM, et al. Prognostic accuracy of qSOFA score, SIRS criteria, and EWSs for in-hospital mortality among adult patients presenting with suspected infection to the emergency department (PASSEM) Multicenter prospective external validation cohort study protocol. PLoS One. 2024;19(1):e0281208.\u003c/li\u003e\n \u003cli\u003eMyrstad M, Ihle-Hansen H, Tveita AA, Andersen EL, Nyg\u0026aring;rd S, Tveit A, et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2020;28(1):66.\u003c/li\u003e\n \u003cli\u003eRahman NA, Chan CM, Zakaria MI, Jaafar MJ. Knowledge and attitude towards identification of systemic inflammatory response syndrome (SIRS) and sepsis among emergency personnel in tertiary teaching hospital. Australas Emerg Care. 2019;22(1):13-21.\u003c/li\u003e\n \u003cli\u003eRios EM, Breda KL. Time Is Survival: Continuing Education on Sepsis for Neurosurgical Critical Care Nurses. J Contin Educ Nurs. 2024;55(5):224-30.\u003c/li\u003e\n \u003cli\u003eBriegel J, M\u0026ouml;hnle P. [Surviving Sepsis Campaign update 2018: the 1 h bundle : Background to the new recommendations]. Anaesthesist. 2019;68(4):204-7.\u003c/li\u003e\n \u003cli\u003eLevy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Intensive Care Med. 2018;44(6):925-8.\u003c/li\u003e\n \u003cli\u003eGripp L, Raffoul M, Milner KA. Implementation of the Surviving Sepsis Campaign one-hour bundle in a short stay unit: A quality improvement project. Intensive Crit Care Nurs. 2021;63:103004.\u003c/li\u003e\n \u003cli\u003eKumar P, Jordan M, Caesar J, Miller S. Improving the management of sepsis in a district general hospital by implementing the \u0026lsquo;Sepsis Six\u0026rsquo; recommendations. BMJ Qual Improv Rep. 2015;4(1).\u003c/li\u003e\n \u003cli\u003eMoore WR, Vermuelen A, Taylor R, Kihara D, Wahome E. Improving 3-Hour Sepsis Bundled Care Outcomes: Implementation of a Nurse-Driven Sepsis Protocol in the Emergency Department. J Emerg Nurs. 2019;45(6):690-8.\u003c/li\u003e\n \u003cli\u003eAhmed AM, Macapili E, Brenner MJ, Pandian V. Accelerating Detection and Intervention for Sepsis in Skilled Nursing Facilities Using a Sepsis Pathway. J Nurs Care Qual. 2024;39(1):67-75.\u003c/li\u003e\n \u003cli\u003eLiu C, Liu Y, Tian Y, Zhang K, Hao G, Shen L, et al. Application of the PDCA cycle for standardized nursing management in sepsis bundles. BMC Anesthesiol. 2022;22(1):39.\u003c/li\u003e\n \u003cli\u003eGao JL, Liu XM, Che WF, Xin X. Construction of nursing-sensitive quality indicators for haemodialysis using Delphi method. J Clin Nurs. 2018;27(21-22):3920-30.\u003c/li\u003e\n \u003cli\u003eYang S, Huang L-H, Zhao X-H, Xing M-Y, Shao L-W, Zhang M-Y, et al. Using the Delphi method to establish nursing-sensitive quality indicators for ICU nursing in China. Research in Nursing \u0026amp; Health. 2019;42(1):48-60.\u003c/li\u003e\n \u003cli\u003eTevik K, Helvik AS, Stensvik GT, Nordberg MS, Nakrem S. Nursing-sensitive quality indicators for quality improvement in Norwegian nursing homes - a modified Delphi study. BMC Health Serv Res. 2023;23(1):1068.\u003c/li\u003e\n \u003cli\u003eSullivan CE, Day SW, Ivankova N, Markaki A, Patrician PA, Landier W. Establishing nursing-sensitive quality indicators for pediatric oncology: An international mixed methods Delphi study. J Nurs Scholarsh. 2023;55(1):388-400.\u003c/li\u003e\n \u003cli\u003eDonabedian A. Quality assessment and assurance: unity of purpose, diversity of means. Inquiry. 1988;25(1):173-92.\u003c/li\u003e\n \u003cli\u003eDonabedian A. The quality of care. How can it be assessed? Jama. 1988;260(12):1743-8.\u003c/li\u003e\n \u003cli\u003eBonfill X, Roqu\u0026eacute; M, Aller MB, Osorio D, Foradada C, Vives A, et al. Development of quality of care indicators from systematic reviews: the case of hospital delivery. Implement Sci. 2013;8:42.\u003c/li\u003e\n \u003cli\u003eBurston S, Chaboyer W, Gillespie B. Nurse-sensitive indicators suitable to reflect nursing care quality: a review and discussion of issues. J Clin Nurs. 2014;23(13-14):1785-95.\u003c/li\u003e\n \u003cli\u003eShen Z, Qin W, Zhu L, Lin Y, Ling H, Zhang Y. Construction of nursing-sensitive quality indicators for cardiac catheterisation: A Delphi study and an analytic hierarchy process. J Clin Nurs. 2022;31(19-20):2821-38.\u003c/li\u003e\n \u003cli\u003eDodd M, Janson S, Facione N, Faucett J, Froelicher ES, Humphreys J, et al. Advancing the science of symptom management. J Adv Nurs. 2001;33(5):668-76.\u003c/li\u003e\n \u003cli\u003eBraun V, Clarke V. Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health. 2019;11(4):589-97.\u003c/li\u003e\n \u003cli\u003eMeskell P, Murphy K, Shaw DG, Casey D. Insights into the use and complexities of the Policy Delphi technique. Nurse Res. 2014;21(3):32-9.\u003c/li\u003e\n \u003cli\u003eHsu C-C, Sandford B. The Delphi Technique: Making Sense Of Consensus. Practical Assessment, Research and Evaluation. 2007;12.\u003c/li\u003e\n \u003cli\u003eGnatzy T, Warth J, von der Gracht H, Darkow I-L. Validating an innovative real-time Delphi approach - A methodological comparison between real-time and conventional Delphi studies. Technological Forecasting and Social Change. 2011;78(9):1681-94.\u003c/li\u003e\n \u003cli\u003eByrne D. A worked example of Braun and Clarke\u0026rsquo;s approach to reflexive thematic analysis. Quality \u0026amp; Quantity. 2022;56(3):1391-412.\u003c/li\u003e\n \u003cli\u003eLasater KB, Sloane DM, McHugh MD, Cimiotti JP, Riman KA, Martin B, et al. Evaluation of hospital nurse-to-patient staffing ratios and sepsis bundles on patient outcomes. Am J Infect Control. 2021;49(7):868-73.\u003c/li\u003e\n \u003cli\u003eKleinpell R, Buchman TG, Harmon L, Nielsen M. Promoting Patient- and Family-Centered Care in the Intensive Care Unit: A Dissemination Project. AACN Adv Crit Care. 2017;28(2):155-9.\u003c/li\u003e\n \u003cli\u003eDeGregoris JP, Bandong L, White T, Brennan MM. Quality Improvement to Promote Sepsis Reassessment: The Sepsis Reassessment Protocol Improvement Project (SRPIP). J Nurs Care Qual. 2023;38(2):107-13.\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;zt\u0026uuml;rk Birge A, Karabag Aydin A, K\u0026ouml;roğlu \u0026Ccedil;amdeviren E. Intensive care nurses\u0026rsquo; awareness of identification of early sepsis findings. J Clin Nurs. 2022;31(19-20):2886-99.\u003c/li\u003e\n \u003cli\u003eChua WL, Teh CS, Basri M, Ong ST, Phang NQQ, Goh EL. Nurses\u0026rsquo; knowledge and confidence in recognizing and managing patients with sepsis: A multi-site cross-sectional study. J Adv Nurs. 2023;79(2):616-29.\u003c/li\u003e\n \u003cli\u003eAlaro MG, Ashine TM, Kebede S, Hussien H, Alaro MG, Kechine Tibore T. Knowledge and Associated Factors Towards Sepsis Management Among Nurses Working in the Emergency Department of Public Hospitals in Addis Ababa. SAGE Open Nurs. 2024;10:23779608241274224.\u003c/li\u003e\n \u003cli\u003eMatthaeus-Kraemer CT, Thomas-Rueddel DO, Schwarzkopf D, Rueddel H, Poidinger B, Reinhart K, et al. Crossing the handover chasm: Clinicians\u0026rsquo; perceptions of barriers to the early detection and timely management of severe sepsis and septic shock. J Crit Care. 2016;36:85-91.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. WHO Director-General\u0026rsquo;s opening remarks at Global Consultation (Virtual) \u0026ldquo;Partners in action: Engaging stakeholders for implementing the Global Patient Safety Action Plan 2021\u0026ndash;2030\u0026rdquo;. Available at https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-global-consultation-virtual-partners-in-action-engaging-stakeholders-for-implementing-the-global-patient-safety-action-plan-2021-2030. Accessed September 18, 20252021.\u003c/li\u003e\n \u003cli\u003eKo BS, Choi SH, Shin TG, Kim K, Jo YH, Ryoo SM, et al. Impact of 1-Hour Bundle Achievement in Septic Shock. J Clin Med. 2021;10(3).\u003c/li\u003e\n \u003cli\u003eChua WL, Rusli KDB, Aitken LM. Early warning scores for sepsis identification and prediction of in-hospital mortality in adults with sepsis: A systematic review and meta-analysis. J Clin Nurs. 2024;33(6):2005-18.\u003c/li\u003e\n \u003cli\u003eReich EN, Then KL, Rankin JA. Barriers to Clinical Practice Guideline Implementation for Septic Patients in the Emergency Department. J Emerg Nurs. 2018;44(6):552-62.\u003c/li\u003e\n \u003cli\u003eRamos Corr\u0026ecirc;a Pinto L, Azzolin KO, Lucena AF, Moretti MMS, Haas JS, Moraes RB, et al. Septic shock: Clinical indicators and implications to critical patient care. J Clin Nurs. 2021;30(11-12):1607-14.\u003c/li\u003e\n \u003cli\u003eHaddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev. 2022;18(2):e1230.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 11 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Delphi method, sepsis management, nursing-sensitive quality indicator","lastPublishedDoi":"10.21203/rs.3.rs-8146381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8146381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCurrently, there are no objective, scientific, and sensitive assessment frameworks for evaluating sepsis care quality management in China. This study aimed to address this gap by developing and validating nursing-sensitive quality indicators (NSQIs) tailored to sepsis management in Chinese clinical settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis mixed-methods investigation involved three phases. A systematic literature review (2014\u0026ndash;2024) across four databases identified evidence regarding sepsis care quality. Subsequently, semi-structured interviews were conducted with 16 clinical experts from emergency departments (EDs) and intensive care units (ICUs) to examine their perspectives on NSQIs. Finally, a modified Delphi process engaged a multidisciplinary panel to refine and validate sepsis-specific NSQIs through a systematic consensus-building process.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eTwo rounds of expert consultation were completed, with a 100% questionnaire return rate. Sixteen experts (10 nurses and six physicians) participated in the first and second rounds of the Delphi survey. The mean score of the expert authority coefficient for the two rounds was 0.96. The range of Kendall W values was 0.120\u0026ndash;0.316 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A comprehensive list of sensitive indicators for sepsis care quality was established, encompassing three primary, nine secondary, and 30 tertiary indicators.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe established NSQIs encompass three fundamental dimensions of sepsis care quality: importance, rationality, and operational feasibility. This two-round Delphi process achieved expert consensus, confirming the system\u0026rsquo;s alignment with clinical guidelines and feasibility.\u003c/p\u003e\u003ch2\u003eClinical practice implications:\u003c/h2\u003e \u003cp\u003eThis research provides a valuable framework for evaluating clinical care quality in sepsis management.\u003c/p\u003e","manuscriptTitle":"Construction of a nursing sensitive quality indicator evaluation system for sepsis care quality: A three-stage mixed-method Delphi study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 11:05:21","doi":"10.21203/rs.3.rs-8146381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-27T16:21:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117912933017707416854673338448329831471","date":"2026-01-27T15:27:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T13:29:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-26T04:42:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T04:29:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T03:39:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-12-05T03:33:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"176fd421-53ec-402a-8df6-d33bb4324315","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T11:05:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 11:05:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8146381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8146381","identity":"rs-8146381","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}